Background subtraction is one of the key pre-processing steps necessary for obtaining relevant information from a video sequence. The selection of a background subtraction algorithm and its parameters is also important for achieving optimal detection performance, especially in night environments. The research contribution presented in this paper is the identification of the optimal background subtractor algorithm in indoor night-time environments, with a focus on the detection of human falls. 30 background subtraction algorithms are analyzed to determine which has the best performance in indoor night-time environments. Genetic algorithms have been applied to identify the best background subtraction algorithm, to optimize the background subtractor parameters and to calculate the optimal number of pre- and post-processing operations. The results show that the best algorithm for fall-detection in indoor, night-time environments is the LBAdaptativeSOM, optimal parameters and processing operations for this algorithm are reported.

Introduction

The risk of falling is one of the most prevalent problems faced by elderly individuals. A study published by the World Health Organization [1] estimates that between 28% and 35% of people over the age of 65 suffer at least one fall each year, and this figure increases to 42% for people over 70. According to the World Health Organization, falls represent greater than 50% of elderly hospitalizations and approximately 40% of nonnatural mortalities for this segment of the population. Falls are a significant source of mortality for elderly individuals in developed countries. Falls are particularly dangerous for people that live alone because of the amount of time that can pass before they receive assistance. Approximately onethird of the elderly (those over the age of 65) in Europe live alone [2], and the elderly population is expected to increase significantly over the next twenty years. The fall detection system proposed by Fallert [3] is based on a low-cost device comprising an embedded computer and a camera. Installed into walls or ceilings, this device monitors a room without human intervention. Thus, people monitored at home are not required to wear devices, and the system is capable of 24 h monitoring. Fallert’s fall detection system works relatively well (over 96% accuracy) during daylight, but performs poorly at night because of the lack of light. To solve this problem, the inclusion of an infrared emitter and a camera without an IR filter were required. Improvements to the background subtractor algorithm used previously [3] were required because of poor performance under night-time conditions.